With reference to FIG. 1, cellular networks typically includes a plurality of adjacent cells 100, each of which is managed by a centralized scheduling device 102, commonly referred to as a base station (“BS”), which communicates with subscribers 104 that are located within the cell 100 and connected to the BS 102. The subscribers 104 are commonly referred to as user equipment (“UE”).
Each UE transmits and receives data to external networks through the BS, which tightly controls what, when, and how the UE's are allowed to transmit and receive. When a UE sends data to the BS, commonly referred to as an “uplink,” it first requests scheduling resources from the BS, and then waits for its scheduling grant before it actually transmits. The BS allocates certain blocks in time and/or frequency to the UE, and determines the best operating parameters for the UE to use when transmitting. The operating parameters typically include the transmission power, communication frequency, time slot, and spreading code allocated to the UE, as well as instructions regarding spectral efficiency parameters such as which modulation and coding scheme (“MCS”) to use. In addition, the operating parameters may include other parameters that affect the transmission of the signal.
The base station selects and updates the operating parameters for all of the UE's in the cell according to a set of base station operating rules, which are configured to balance and optimize various desirable characteristics of the network. These can include network capacity, fairness, Quality of Service (“QoS”), and such like.
In particular, the base station must take into account background noise and interference when assigning operating parameters to the UE's. For example, if a certain frequency band has low background interference, the BS may choose to lower the transmit power of the UE assigned to that channel, and/or may instruct the UE to use an efficient but relatively fault intolerant MCS. On the other hand, if the background noise and interference in a certain frequency band is high, and if the BS needs to use that frequency band so as to accommodate all of the UE's in the cell, then the base station may assign a UE to a channel in that frequency band, and instruct the UE to use a relatively higher transmitting power and/or a slower but more fault tolerant MCS.
Often, background interference is a major component of the overall background noise and interference. Background interference arises from transmissions that stray into the cell from UE's in adjacent cells. Referring again to FIG. 1, it is clear that UE's located near cell boundaries can be physically close to each other, even though they are located in different cells. Since the UE signals are transmitted omni-directionally, transmissions from UE's near cell boundaries will stray into neighboring cells. And because UE's in different cells are managed by different base stations, there is a high likelihood that some of these stray signals will interfere with communications in the adjacent cells.
Accordingly, a BS typically selects and assigns operating parameters to a UE according to a predicted Signal to Interference and Noise Ratio (SINR) for a selected communication channel. Typically, the BS will make measurements of background interference, or of background interference-plus-noise, and will use these measurements in predicting future interference-plus-noise. These predictions may be based on single measurements, but often a plurality of measurements of background interference-plus-noise are made before each SINR prediction, producing a distribution of interference measurements that can be represented as a histogram, possibly with an approximately Gaussian distribution. The prediction of future interference-plus-noise is then based on an average of the measurements, with a margin added to ensure quality of service, or possibly on the shape and width of the histogram.
Once the predicted SINR is determined, appropriate operating parameters are selected and transmitted to the UE's. This process is repeated periodically by making new predictions based on new measurements of the background interference or background interference-plus-noise, updating the operating parameters, and transmitting the updated operating parameters to the UE's. Note that selection of the operating parameters may also be affected by other factors, such as the number of simultaneous users, etc.
As noted above, background interference is often significant and much larger than the noise. Unfortunately, background interference in a cell can vary rapidly, as new UE's initiate or cease communications with base stations in neighboring cells, and as the neighboring base stations make changes to the operating parameters of their UE's, often in response to rapid fluctuations in background interference experienced by these neighboring cells.
These rapid fluctuations in background interference levels can lead to large variances between the estimated background interference levels, based on measurements made at an earlier time, and the actual background interference levels experienced when the signal arrives at a later time. For example, a typical histogram 300 of background interference measurements in the prior art is shown in FIG. 3. The histogram 300 is the result of performing a large number of repeated measurements of background interference, and plotting a curve 300 that shows the relative number of measurement results corresponding to each value of background interference. A typical MCS selection algorithm will then choose an expected level of background interference based on the average background interference plus some margin, so as to ensure quality of service. This expected background interference level will be used to calculate an expected SINR, which is then used for selecting the MCS.
The curve in FIG. 3 is approximately Gaussian, but other shapes may occur in practice. This broad histogram 300 of the prior art can be divided into two regions 302, 304, according to the accuracy of the prediction of background interference. Line 306 represents the background interference prediction that a base station 102 will likely make based on the histogram 300 of previous background interference measurements, which is the average interference level plus some margin. The base station 102 will then calculate an expected SINR from the expected interference level and choose appropriate operating parameters.
If the actual background interference is in region 302 when the UE 104 later transmits, ie. is less than the predicted background interference level 306, and hence the SINR is actually higher than expected, the base station 102 could have successfully chosen operating parameters for UE 104 to obtain higher spectral efficiency. If the actual background interference is in the region 304 when the UE 104 later transmits, ie. is greater than the predicted interference level 306, and hence the SINR is actually lower than expected, the UE 104 to likely to have a packet error necessitating a packet retransmission.
Therefore any deviation of the actual background interference from the predicted background interference causes the base station 102 to allocate non optimal operating parameters to the UE 104. Accordingly, the quality and efficiency with which a cell is managed depends to a significant extent on the accuracy of the background interference predictions made by its base station.
One approach to improving this situation is for the BS to transmit modified operating parameters to a UE shortly after the UE has transmitted one or more times to the BS, where the modified operating parameters are based on a bit error rate, packet error rate, or another error rate experienced during these initial transmissions. However, this approach still suffers from inaccurate predictions of the SINR, due to the very rapid fluctuations of the background interference.
Many networks have the ability to operate with Semi-Persistent Scheduling (SPS). SPS occurs when a UE is instructed to use a given set of operating parameters for more than a single transmission. SPS can be advantageous, because most traffic in a cell is not in the form of tiny bursts of data, and so the scheduling overhead to update the operating parameters for each packet could otherwise be needlessly burdensome. SPS can also provide some improvement in the SINR estimates, because the measurements of background interference or background interference-plus-noise can be made over longer periods of time, thereby providing better averaging of the background interference fluctuations.
However, even predictions of background interference based on longer averaging periods can be unreliable. Also, it may not be possible to implement SPS in a given network, due to other factors and priorities of the network. For example, many networks include base station operating rules that require that the UE's be “hopped” rapidly between communication frequency bands, so that each of the UE's experiences approximately the same QoS.
Of course, if the base stations were able to communicate directly and fully with each other, so that each base station knew in advance what the others were planning to do, then predictions of background interference could be significantly improved. However, such comprehensive inter-BS communication is typically prohibitive.
What is needed, therefore, is a method for improving the operating efficiency and quality of service in a cellular communications network by improving the accuracy of the SINR predictions made by the base stations.